P470: SINGLE-CELL METABOLIC MULTIOMICS TO IDENTIFY VULNERABILITIES IN ACUTE MYELOID LEUKEMIA STEM CELLS
Authors: Magdalena Antes, Eleni Besiridou, Maximilian Mönnig, Sergi Beneyto Calabuig, Andreas Trumpp, Carsten Müller-Tidow, Simon Haas, Lars Velten, Simon Raffel
Journal: HemaSphere (2023)

Background: Acute myeloid leukemia (AML) is an aggressive myeloid malignancy, which is characterized by substantial inter and intra-patient heterogeneity. While treatment regimens lead to remission in most patients, relapse rates are high and overall survival remains poor. Rare leukemic stem cells (LSCs), the driving force of relapse, display specific metabolic features, differing not only from leukemic blasts but also from hematopoietic stem cells. LSCs rely on oxidative phosphorylation rather than glycolysis for their energy production preferentially using amino acids or fatty acids as energy source. Moreover, LSCs maintain low levels of reactive oxygen species. With improving single-cell (sc) technologies, clonal composition and cellular heterogeneity even with the LSC pool can be addressed. Aims: Aim of the study is to identify metabolic vulnerabilities in LSCs at single cell resolution. We introduce a novel multiomics approach to simultaneously measure metabolic properties, transcriptome, mutational status as well as in vitro clonogenic capacity at sc-level. Methods: In this study, we developed a novel flow cytometry panel measuring known surface markers for LSC discrimination together with key metabolic properties at sc-level to characterise a plethora of AML samples. For a deeper, mechanistic understanding of observed metabolic phenotypes, a smaller cohort of patients was further subjected to metabolic multiomics by index-sort paired with sc-RNAseq using MutaSeq and sc-cultures to simultaneously measure metabolic properties, gene expression, mutational status and clonogenic capacity. For data analysis and integration, we applied and adapted various bioinformatic tools such as Scanorama, Seurat, CloneTracer as well as linear regression models to connect culture with RNA data. Results: Applying our newly established metabolic flow panel, we analysed a large cohort of de novo AML patients of various AML subtypes. We observed subtype-specific metabolic differences both at bulk and within phenotypically defined LSCs and blasts populations. Identified metabolic profiles correlate with overall survival in patients as well as with leukemic engraftment levels in xenotransplant experiments. In our sc-dataset, we were able to reconstruct clones and to discriminate leukemic, pre-leukemic and healthy cells. By applying a supervised machine learning approach, a growth probability score was calculated for each individual cell in the sc-RNAseq dataset. Data integration of all layers of information allowed us to identify transcriptomic and metabolic signatures in LSCs and to connect these to their in vitro growth potential. Summary/Conclusion: Cellular heterogeneity is one of the main driver of therapy-resistance in AML. To address metabolic, transcriptomic and genetic heterogeneity, we established a single cell multiomics approach that allows the simultaneous measurement of metabolic properties, gene expression, mutation status as well as in vitro clonogenic capacity of individual cells. We identify metabolic and transcriptomic signatures that correlate with stemness properties and function. Interfering with metabolic dependencies of leukemic stem cells may pave the way for developing more effective treatments. Keywords: Acute myeloid leukemia, OMICS, Molecular markers, Leukemic stem cell